Total disability days in interprovincial and home‐province workers injured in Alberta, Canada: A mixed‐methods study with matched‐pair analysis of compensation data and participant interviews
Bibliographic record
Abstract
INTRODUCTION: Workers moving between states or provinces to find employment are reported to take longer to return to work after the injury. METHODS: The Alberta Workers Compensation Board (WCB) identified all workers from four Canadian Atlantic provinces who sustained a work injury in Alberta resulting in greater than 5 total temporary disability days (TTDDays) from January 2015 to June 2017. Each was matched on sex, age, and injury date with an Alberta claimant also with greater than 5 TTDDays. WCB information extracted included employment, injury, cost and place of treatment, and modified work. Cox regression identified factors associated with TTDDays. Semi-structured interviews were also undertaken. RESULTS: Two-hundred forty pairs were identified and 60 interviews completed. Those from the Atlantic provinces had more TTDDays (median 63 days) than Alberta (median 22 days) with an unadjusted hazard ratio (HR) 0.50 (95% confidence interval [CI], 0.42-0.61). When adjusted for all factors, the HR moved closer to unity (HR = 0.62; 95% CI, 0.50-0.76). Total health care costs were the strongest predictor, with modified work, injury type, and claim status also explanatory factors. Among the Atlantic workers, leaving Alberta for treatment was strongly related to a lower likelihood of ending wage replacement (HR = 0.45; 95% CI, 0.32-0.62). Participants in the interview study emphasized the importance of returning to the family after injury and the financial difficulties of maintaining a second home with reduced income after the injury. CONCLUSION: The higher costs of wage replacement associated with extended time off work may be inherent to the practice of employing out-of-province workers for jobs for which there is a shortage of local labor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".